A Proposed Novel Hybrid Intelligent Model Based on ANFIS Integrated with Firefly Algorithm for Forecasting Discharge Coefficient of Side Weirs on Converging Canals *

2020 ◽  
Vol 69 (4) ◽  
pp. 865-879
Author(s):  
Majeid Heydari ◽  
Saeid Shabanlou
Keyword(s):  
Firefly Algorithm ◽  
Discharge Coefficient ◽  
Model Based ◽  
Side Weirs ◽  
Intelligent Model

Neural Network Based Control of Preform Permeation in Resin Transfer Molding Processes With Real-Time Permeability Estimation

2000 ◽  
Author(s):  
David Nielsen ◽  
Ranga Pitchumani
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Resin Transfer Molding ◽  
Local Pressure ◽  
Transfer Molding ◽  
Model Based ◽  
Rtm Process ◽  
Virtual Sensing ◽  
Intelligent Model ◽  
Injection Pressures

Abstract Variabilities in the preform structure in situ in the mold are an acknowledged challenge to effective permeation control in the Resin Transfer Molding (RTM) process. An intelligent model-based controller is developed which utilizes real-time virtual sensing of the permeability to derive optimal decisions on controlling the injection pressures at the mold inlet ports so as to track a desired flowfront progression during resin permeation. This model-based optimal controller employs a neural network-based predictor that models the flowfront progression, and a simulated annealing-based optimizer that optimizes the injection pressures used during actual control. Preform permeability is virtually sensed in real-time, based on the flowfront velocities and local pressure gradient estimations along the flowfront. Results are presented which illustrate the ability of the controller in accurately steering the flowfront for various fill scenarios and preform geometries.

Implementation of an Intelligent Model Based on Machine Learning in the Application of Macro-Ergonomic Methods in a Human Resources Process Based on ISO 12207

2022 ◽  
pp. 251-275
Author(s):  
Edgar Cossio Franco ◽  
Jorge Alberto Delgado Cazarez ◽  
Carlos Alberto Ochoa Ortiz Zezzatti
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Decision Trees ◽  
Work Stress ◽  
Selection Process ◽  
Model Based ◽  
Java Language ◽  
Intelligent Model

The objective of this chapter is to implement an intelligent model based on machine learning in the application of macro-ergonomic methods in human resources processes based on the ISO 12207 standard. To achieve the objective, a method of constructing a Java language algorithm is applied to select the best prospect for a given position. Machine learning is done through decision trees and algorithm j48. Among the findings, it is shown that the model is useful in identifying the best profiles for a given position, optimizing the time in the selection process and human resources as well as the reduction of work stress.

scholarly journals Are We Correctly Using Discharge Coefficients for Side Weirs? Insights from a Numerical Investigation

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2585
Author(s):  
Mario Di Bacco ◽  
Anna Rita Scorzini
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Discharge Coefficient ◽  
Experimental Methodology ◽  
Laboratory Measurements ◽  
Experimental Assessment ◽  
Discharge Coefficients ◽  
Side Weirs ◽  
Water Depths

A key issue in the design of side weirs is the experimental assessment of the discharge coefficient. This can be determined by laboratory measurements of discharge and water depths at the up- and downstream ends of the weir by using De Marchi’s approach, consisting in the solution of the 1D dynamic equation of spatially varied steady flow with non-uniform discharge, under the assumption of energy conservation. This study originates from a recent alarming proliferation of works that evaluate the discharge coefficient for side weirs without clearly explaining the experimental methodology and/or even incorrectly applying modelling approaches, thus generating possible misinterpretations of the results. In this context, the present paper aims to highlight the effects of using oversimplified and/or heterogenous models (relying on different assumptions) for the experimental determination of the discharge coefficient for side weirs. Furthermore, a sensitivity analysis is performed to detect the most influencing hydraulic and geometric parameters on each considered model. The overall results clearly indicate the wrongness of using or building not homogeneous discharge coefficient datasets to obtain and/or compare predictive experimental discharge coefficient formulas. We finally show how neural networks could provide a possible solution to these heterogeneity issues.

scholarly journals Optimization of ANFIS network using firefly algorithm for simulating discharge coefficient of side orifices

2019 ◽  
Vol 9 (4) ◽  
Author(s):  
Rahim Gerami Moghadam ◽  
Mohammad Ali Izadbakhsh ◽  
Fariborz Yosefvand ◽  
Saeid Shabanlou
Keyword(s):  
Firefly Algorithm ◽  
Discharge Coefficient

scholarly journals Determination Discharge Capacity of Triangular Labyrinth Side Weir Using Multi-Layer Neural Network (ANN-MLP)

2015 ◽  
Vol 10 (Special-Issue1) ◽  
pp. 111-119 ◽  
Author(s):  
Sohrab Karimi ◽  
Hossein Bonakdari ◽  
Azadeh Gholami
Keyword(s):  
Discharge Capacity ◽  
Discharge Coefficient ◽  
Vertex Angle ◽  
Hydraulic Parameters ◽  
Side Weir ◽  
Dimensionless Parameters ◽  
Weir Height ◽  
Crest Length ◽  
Side Weirs ◽  
Mlp Model

statistic indexes have been used to assess the accuracy of the results. The results of the examinations indicate that using MLP model along with simultaneous use of dimensionless parameters for the purposes of estimating discharge coefficient: the ratio of water behind the weir to the channel width (h/b), ratio of weir crest length to weir height (L/W), relative Froude number (F=V/√(2Side weirs are used in open channels to control flood and the flow passing through it. Discharge capacity is one of the crucial hydraulic parameters of side weirs. The aim of this study is to determine the effect of the intended dimensionless parameters on predicting the discharge coefficient of triangular labyrinth side weir. MAPE, RMSE, and Rgy)) and vertex angle (ϴ), offered the best results (MAPE= 0.67, R2= 0.99, RMSE = 0.009) in comparison with other models.

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